U.S. patent number 6,785,676 [Application Number 09/778,139] was granted by the patent office on 2004-08-31 for customer self service subsystem for response set ordering and annotation.
This patent grant is currently assigned to International Business Machines Corporation. Invention is credited to Daniel A. Oblinger.
United States Patent |
6,785,676 |
Oblinger |
August 31, 2004 |
Customer self service subsystem for response set ordering and
annotation
Abstract
A system and method for annotating resource results obtained in
a customer self service system that performs resource search and
selection. The method comprising the steps of: receiving a resource
response set of results obtained in response to a current user
query and receiving a user context vector associated with the
current user query, the user context vector comprising data
associating an interaction state with the user; applying an
ordering and annotation function for mapping the user context
vector with the resource response set to generate an annotated
response set having one or more annotations; and, controlling the
presentation of the resource response set to the user according to
the annotations, wherein the ordering and annotation function is
executed interactively at the time of each user query. In an
off-line process, a supervised learning algorithm is implemented
for receiving user interaction data from among a database of user
interaction records and an annotation scoring metric representing a
measure of performance in locating resource response results
displayed via a graphical interface. The algorithm generates
ordering and annotation functions which are adaptable based on
history of user interactions as provided in the database of user
interaction records. The result of this invention is the ability to
drive visualization systems for presenting resource response
results in the most beneficial and meaningful way to the user via
an interface when performing search and resource selection. The
system and method is especially applicable for a self service
system in a variety of customer self service domains including
education, real estate and travel.
Inventors: |
Oblinger; Daniel A. (New York,
NY) |
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
25112414 |
Appl.
No.: |
09/778,139 |
Filed: |
February 7, 2001 |
Current U.S.
Class: |
1/1; 707/999.004;
707/E17.143; 715/209; 707/999.005 |
Current CPC
Class: |
G06Q
30/02 (20130101); G06Q 30/06 (20130101); G06F
16/907 (20190101); Y10S 707/99934 (20130101); Y10S
707/99935 (20130101) |
Current International
Class: |
G06F
17/30 (20060101); G06F 7/00 (20060101); G06F
17/00 (20060101); G06F 017/30 (); G06F
017/00 () |
Field of
Search: |
;707/1-8,101-102,104.1
;345/700-702,748,744,707-708 ;715/512 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
"The Answer Machine" (Information Services Management) (Industry
Trend or Event), by Susan Feldman, Jan. 2000, The Magazine for
Database Professionals, 41 pages. .
Billsus, D., et al., "A learning agent for wireless news access,"
Proceedings of IUI 2000: International Conference on Intelligent
User Interfaces, ACM, Jan. 9-12, 2000, pp. 33-36, XP-002205011.
.
Olsen, K., et al., "Visualization of a Document Collection: The
Vibe System", Information Processing & Management, Elsevier,
Barking, GB, vol. 29, No. 1, 1993, pp. 69-81, XP 000574984. .
MIadenic, D, "Text-learning and related intelligent agents: a
survey", IEEE Intelligent Systems, IEEE, vol. 14, No. 4, Jul. 1999,
pp. 44-54, XP-002205012. .
Goker, A., "Capturing Information Need by Learning User Context",
16th International Joint Conferent in Artificial Intelligence:
Learning About User Workshop, Jul. 31, 1999, pp. 21-27,
XP-002205013. .
Anonymous, "Taxonomized Web Search", IBM Technical Disclosure
Bulletin, IBM Corp. New York, US, vol. 40, No. 5, May 1, 1997, pp.
195-196, XP-002133594. .
Davies, J., et al., "Knowledge Discovery and Delivery", British
Telecommunications Engineering, London, GB, vol. 17, No. 1, Apr. 1,
1998, pp. 25-35, XP-000765546..
|
Primary Examiner: Kindred; Alford
Attorney, Agent or Firm: Scully, Scott, Murphy & Presser
Morris, Esq.; Daniel P.
Claims
Having thus described my invention, what I claim as new, and desire
to secure by Letters Patent is:
1. A resource results annotator for a customer self service system
that performs resource search and selection comprising: mechanism
for receiving a resource response set of results obtained in
response to a current user query; mechanism for receiving a user
context vector associated with said current user query, said user
context vector comprising data associating an interaction state
with said user and including context that is a function of the
user; and, an ordering and annotation function for mapping the user
context vector with the resource response set to generate an
annotated response set having one or more annotations for
controlling the presentation of the resources to the user, wherein
the ordering and annotation function is executed interactively at
the time of each user query.
2. The resource results annotator as claimed in claim 1, wherein
said annotations include elements for ordering resource results to
be displayed via a graphical user interface.
3. The resource results annotator as claimed in claim 1, wherein
said annotations include elements for bolding one or more resource
results to be displayed via a graphical user interface.
4. The resource results annotator as claimed in claim 1, wherein
said annotations include elements for determining one or more
primary resource results to be displayed on a first display screen
via a graphical user interface and which are secondary resource
results for presentation via a secondary display screen.
5. The resource results annotator as claimed in claim 1, wherein
said self service system includes a database of user interaction
records including actual resources selected by the users and the
annotation schemes used for presenting them via a graphical
interface, said annotator further comprising a processing mechanism
for receiving user interaction data from among said database of
user interaction records and an annotation scoring metric
representing a measure of performance in locating resource response
results displayed via said graphical interface, and, generating
said ordering and annotation function, said annotation function
being adaptable based on history of user interactions as provided
in said database of user interaction records.
6. The resource results annotator as claimed in claim 1, wherein
said processing mechanism for generating said ordering and
annotation function is performed off-line.
7. The resource results annotator as claimed in claim 5, wherein
said user interaction data comprises past and present user
queries.
8. The resource results annotator as claimed in claim 5, wherein
said user interaction data comprises system responses to said user
queries.
9. The resource results annotator as claimed in claim 5, wherein
said user interaction data comprises raw context information
including: one or more of static, historical context, transient
context, organizational context, community context, and environment
context.
10. The resource results annotator as claimed in claim 9, wherein
said user interaction data comprises other raw context associated
with the user and dependent upon that user's interaction state and
query domain.
11. The resource results annotator as claimed in claim 10, wherein
a query domain includes one of: education, travel and real
estate.
12. The resource results annotator as claimed in claim 5, wherein
said processing mechanism implements a supervised learning
algorithm.
13. The resource results annotator as claimed in claim 12, wherein
said user interaction data comprises user interaction feedback,
said supervised learning algorithm optimizing said annotation
scoring metric as measured by said user interaction feedback.
14. A method for annotating resource results obtained in a customer
self service system that performs resource search and selection,
said method comprising the steps of: a) receiving a resource
response set of results obtained in response to a current user
query; b) receiving a user context vector associated with said
current user query, said user context vector comprising data
associating an interaction state with said user and including
context that is a function of the user; c) applying an ordering and
annotation function for mapping the user context vector with the
resource response set to generate an annotated response set having
one or more annotations; and, d) controlling the presentation of
the resource response set to the user according to said
annotations, wherein the ordering and annotation function is
executed interactively at the time of each user query.
15. The method as claimed in claim 14, wherein said controlling
step d) further includes the step of bolding one or more resource
results to be displayed via a graphical user interface.
16. The method as claimed in claim 14, wherein said controlling
step d) further includes the step of determining one or more
primary resource results to be displayed on a first display screen
via a graphical user interface and which are secondary resource
results for presentation via a secondary display screen.
17. The method as claimed in claim 14, wherein said self service
system includes a database of user interaction records including
actual resources selected by the users and the annotation schemes
used for presenting them via a graphical interface, said method
further comprising the steps of: receiving user interaction data
from among said database of user interaction records and an
annotation scoring metric representing a measure of performance in
locating resource response results displayed via said graphical
interface; and, generating said ordering and annotation function,
said annotation function being adaptable based on history of user
interactions as provided in said database of user interaction
records.
18. The method as claimed in claim 17, wherein said step of
generating said ordering and annotation function is performed
off-line.
19. The method as claimed in claim 17, further including
implementing a supervised learning algorithm for generating said
ordering and annotation function.
20. The method as claimed in claim 18, wherein said user
interaction data comprises user interaction feedback, said
supervised learning algorithm optimizing said annotation scoring
metric as measured by said user interaction feedback.
21. A program storage device readable by machine, tangibly
embodying a program of instructions executable by the machine to
perform method steps for annotating resource results obtained in a
customer self service system that performs resource search and
selection, said method comprising the steps of: a) receiving a
resource response set of results obtained in response to a current
user query; b) receiving a user context vector associated with said
current user query, said user context vector comprising data
associating an interaction state with said user and including
context tat is a function of the user; c) applying an ordering and
annotation function for mapping the user context vector with the
resource response set to generate an annotated response set having
one or more annotations; and, d) controlling the presentation of
the resource response set to the user according to said
annotations, wherein the ordering and annotation function is
executed interactively at the time of each user query.
22. The program storage device readable by machine as claimed in
claim 21, wherein said controlling step d) further includes the
step of ordering resources results to be displayed via a graphical
user interface.
23. The program storage device readable by machine as claimed in
claim 21, wherein said controlling step d) further includes the
step of bolding one or more resources results to be displayed via a
graphical user interface.
24. The program storage device readable by machine as claimed in
claim 21, wherein said controlling step d) further includes the
step of determining one or more primary resource results to be
displayed on a first display screen via a graphical user interface
and which are secondary resource results for presentation via a
secondary display screen.
25. The program storage device readable by machine as claimed in
claim 21, wherein said self service system includes a database of
user interaction records including actual resources selected by the
users and the annotation schemes used for presenting them via a
graphical interface, said method further comprising the steps of:
receiving user interaction data from among said database of user
interaction records and an annotation scoring metric representing a
measure of performance in locating resource response results
displayed via said graphical interface; and, generating said
ordering arid annotation function, said annotation function being
adaptable based on history of user interactions as provided in said
database of user interaction records.
26. The program storage device readable by machine as claimed in
claim 21, wherein said step of generating said ordering and
annotation function is performed off-line.
27. The program storage device readable by machine as claimed in
claim 25, further including implementing a supervised learning
algorithm for generating said ordering and annotation function.
28. The method as claimed in claim 26, wherein said user
interaction data comprises user interaction feedback, said
supervised learning algorithm optimizing said annotation scoring
metric as measured by said user interaction feedback.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to the field of customer self
service systems for resource search and selection, and more
specifically, to a novel mechanism for annotating response sets via
an adaptive algorithm, wherein the annotations supplied are used to
drive any visualization system that presents resource response
results.
2. Discussion of the Prior Art
Currently there exist many systems designed to perform search and
retrieval functions. These systems may be classified variously as
knowledge management systems, information portals, search engines,
data miners, etc. However, providing effective customer self
service systems for resource search and selection presents several
significant challenges. The first challenge for current systems
with query capability is that serving queries intelligently
requires a large amount of user supplied contextual information,
while at the same time the user has limited time, patience, ability
and interest to provide it. The second challenge is that searching
without sufficient context results in a very inefficient search
(both user time and system resource intensive) with frequently
disappointing results (overwhelming amount of information, high
percentage of irrelevant information). The third challenge is that
much of a user's actual use and satisfaction with search results
differ from that defined at the start of the search: either because
the users behave contrary to their own specifications, or because
there are other contextual issues at play that have not been
defined into the search. The prior art has addressed the use of
some of the features of the resources (content and other) in
relation to the user's context and/or prior use of other resource
search and selection systems, for selection of responses to current
user's queries. Representative prior art approaches systems
described in U.S. Pat. No. 5,724,567 entitled "System for Directing
Relevance-Ranked Data Objects to Computer Users"; U.S. Pat. No.
5,754,939 entitled "System for Generation of User Profiles For a
System For Customized Electronic Identification of Desirable
Objects"; and, U.S. Pat. No. 5,321,833 entitled "Adaptive Ranking
System for Information Retrieval".
U.S. Pat. No. 5,321,833 describes an adaptive record ranking method
for full text information retrieval, which quantifies the relevance
of retrieved records to query terms occurring in the record. The
method utilizes a multilevel weighting technique which permits user
input to affect record weighting at each level of the ranking
process. The method utilizes weighted attributes of properties of
terms occurring in the records of a database and compensates for
the distance between adjacent words of complex terms. The method
has been implemented on large full text databases and the resulting
rankings achieve a relatively high level of precision in ranking
the relevance of retrieved records to a user query. However, this
method does not take into account user context data, and thus is
not specialized based on a user situation within the whole
system.
U.S. Pat. No. 5,724,567 describes an information access system that
stores items of information in an unstructured global database.
When a user requests access to the system, the system delivers to
that user an identification of only those items of information
which are believed to be relevant to the user's interest. The
determination as to the items of information that are relevant to a
user is carried out by ranking each available item in accordance
with any one or more techniques. In one approach, the content of
each document is matched with an adaptive profile of a user's
interest. In another approach, a feedback mechanism is provided to
allow users to indicate their degree of interest in each item of
information. These indications are used to determine whether other
users, who have similar or dissimilar interests, will find a
particular item to be relevant.
For instance, U.S. Pat. No. 5,754,939 describes a method for
customized electronic identification of desirable objects, such as
news articles, in an electronic media environment, and in
particular to a system that automatically constructs both a "target
profile" for each target object in the electronic media based, for
example, on the frequency with which each word appears in an
article relative to its overall frequency of use in all articles,
as well as a "target profile interest summary" for each user, which
target profile interest summary describes the user's interest level
in various types of target objects. The system then evaluates the
target profiles against the users' target profile interest
summaries to generate a user-customized rank ordered listing of
target objects most likely to be of interest to each user so that
the user can select from among these potentially relevant target
objects, which were automatically selected by this system from the
plethora of target objects that are profiled on the electronic
media.
A major limitation of these prior art approaches, however, is the
absence of a mechanism for implementing user context in informing
the ranking of the resources. Moreover, these prior art approaches
are limited in that they do not enable user tutoring of the
application for ranking information. That is, prior art approaches
do not provide for the adaptation or changing the ranking over
time, for example, based on a history of user interactions with the
system. Another major limitation of the prior art is that these
systems do not annotate the response sets via an adaptive algorithm
and moreover, do not use the resulting annotations to drive
visualization systems.
It would be highly desirable to provide for a customer self service
system, a mechanism that annotates query response sets via an
adaptive algorithm and wherein the annotations the mechanism
supplies may be used to drive any visualization system.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide for a customer
self service system for resource search and selection a mechanism
for supplying annotations to a set of query response sets via an
adaptive algorithm.
It is a further object of the present invention to provide for a
customer self service system for resource search and selection an
annotation mechanism for annotating query response sets wherein the
annotations affect the order that these resources are presented to
the user.
It is another object of the present invention to provide for a
customer self service system for resource search and selection, an
annotation mechanism for annotating query response sets wherein the
annotations provided affect the order that these resources are
presented to the user and wherein the ordering is based on features
of the resource itself when viewed through the user's context.
It is yet another object of the present invention to provide an
annotation function for a customer self service system for resource
search and selection that implements a supervised learning
algorithm wherein training data utilized for this algorithm is
derived from prior user interactions and the annotation function is
optimized based on an annotation scoring metric.
According to the invention, there is provided a system and method
for annotating resource results obtained in a customer self service
system that performs resource search and selection. The method
comprising the steps of: receiving a resource response set of
results obtained in response to a current user query and receiving
a user context vector associated with the current user query, the
user context vector comprising data associating an interaction
state with the user; applying an ordering and annotation function
for mapping the user context vector with the resource response set
to generate an annotated response set having one or more
annotations; and, controlling the presentation of the resources
response set to the user according to the annotations, wherein the
ordering and annotation function is executed interactively at the
time of each user query.
Further, in an off-line process, a supervised learning algorithm is
implemented for receiving user interaction data from among a
database of user interaction records and an annotation scoring
metric representing a measure of performance in locating resource
response results displayed via a graphical interface. The algorithm
generates ordering and annotation functions which are adaptable
based on history of user interactions as provided in the database
of user interaction records. The result of this invention is the
ability to drive any visualization system for presenting resource
response results in the most beneficial and meaningful way to the
user via an interface when performing search and resource
selection.
Advantageously, such a system and method of the invention is
applicable for a customer self service system in a variety of
customer self service domains including education, real estate and
travel.
BRIEF DESCRIPTION OF THE DRAWINGS
Further features, aspects and advantages of the apparatus and
methods of the present invention will become better understood with
regard to the following description, appended claims, and the
accompanying drawings where:
FIG. 1 is a flowchart showing the steps of the control flow between
the components comprising the customer self service system
according to the invention.
FIG. 2 is a flowchart showing the generic process steps of the
user's interaction with the customer self service system through
various iconic interfaces.
FIG. 3 provides examples of data elements from the education, real
estate and travel domains given example user interactions with the
customer self service system via the iconic interfaces.
FIG. 4 illustrates the first iconic Graphical User Interface 12
including the Context Entry Workspace 13.
FIG. 5 illustrates the second iconic Graphical User Interface 22
including the Detail Specification Workspace 23.
FIG. 6 is a flowchart depicting the methodology for adaptive
response ordering and annotation according to the preferred
embodiment of the invention.
FIG. 7 illustrates in detail the third iconic graphical user
interface 32 including the Results Display Workspace 33 that
enables the user to visualize and explore the response set that the
system has found to best match the user's initial query and related
subject and context variables.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
FIG. 1 illustrates a customer self service system ("system") 10
which is described in detail commonly-owned, co-pending U.S. patent
application Ser. No. 09/778,146 entitled CUSTOMER SELF SERVICE
SYSTEM FOR RESOURCE SEARCH AND SELECTION the contents and
disclosure of which are incorporated by reference as if fully set
forth herein. The system 10 is a comprehensive self service system
providing an end-to-end solution that integrates the user and
system, the content and context, and, the search and result so that
the system may learn from each and all users and make that learning
operationally benefit all users over time. The present invention
comprises a particular aspect of this system that focuses on
annotating a set of response resources by implementing a supervised
training algorithm. Particularly, the present invention is directed
to a response set ordering and annotation sub-process that
generates annotations that affect, among other things, the order
that these resources are presented to the user of the resource
search and selection system. The ordering is based on features of
the resource itself when viewed through the user's context.
Particularly, as shown in FIG. 1, the self service system provides
a three-part intuitive iconic interface comprising interface
components 12, 22 and 32 for visualizing and exploring the set of
resources that the system has found to match the user's initial
query and related subject and context variables. The system 10
preferably enables the expression of a user's context as part of
the query and expresses the relevance of the results to a
particular user via the interface in terms beyond that of the
results' content. The resource set is presented to the user in a
way which clearly illustrates their degree of fit with the user's
most important context variables, as indicated by their prior usage
of the system, as well as by context choices for the current query.
The system displays the resources in the sequence specified by the
user and enables the user to select and weight the criteria to be
used in interpreting and selecting between resources. This provides
a shifting of the user's focus from finding something, to making
choices among the set of resources available. Via the interface
components 12, 22 and 32, the user may redefine their query,
preview some or all of the suggested resources or further reduce,
and redisplay the response set to extract those with the best
degree of fit with that user's current needs. The system generates
and displays via the interface a listing of the currently active
inclusionary and exclusionary content filters and provides a means
for modifying them. More specifically, the intuitive user interface
of the invention enables users to specify the variables of their
resource needs.
FIG. 2 particularly depicts reduced-size displays illustrating the
three iconic user interfaces 12, 22, 32 which comprise the
respective workspaces according to the invention. As will be
described in greater detail herein, the first graphical user
interface 12 comprises an initial Context Selection Workspace 13
that enables the expression of user context as part of a query in a
manner optimized for ease of use; the graphical user interface 22
shown in FIG. 2 provides a Detailed Specification Workspace 23
including a visual representation of multi-dimensional data for
expressing query and results that enables users to completely
manage their search in a manner optimized for simplicity and
clarity of logic; and, the graphical user interface 32 is directed
to a Results Display Workspace 33 that enables expression of
relevance of results in terms of user context in a manner optimized
to facilitate resource selection using user supplied decision
criteria. Aspects of interfaces 12, 22 and 32 shown in FIG. 2 are
described in greater detail in commonly-owned, co-pending U.S.
patent application Ser. No. 09,778,136 entitled CUSTOMER SELF
SERVICE ICONIC INTERFACE FOR PORTAL ENTRY AND SEARCH SPECIFICATION
and commonly-owned, co-pending U.S. patent application Ser. No.
09/778,147 entitled CUSTOMER SELF SERVICE ICONIC INTERFACE FOR
RESOURCE SEARCH RESULTS DISPLAY AND SELECTION, the contents and
disclosure of each of which are incorporated by reference as if
fully set forth herein.
Referring back to FIG. 1, there is depicted a conceptual control
flow 10 for the customer self service resource search and selection
system according to a preferred embodiment. Via the three-part
intuitive graphic user interface (GUI) users are enabled to enter
queries and manipulate the system's responses according to their
resource needs. Behind the scenes, as will be described, is a set
of sub-system components that cooperate to derive, assume, sense
and infer particular user contexts with minimal user effort.
These components include databases such as: 1) a Context Attributes
Master database 14 which stores the definitions of all the
attributes known to the system and their relationships to
predefined user contexts; 2) an Attribute Value Functions database
16 which stores the definitions and logic associated with assigning
a value to an attribute for specific instances (context default,
groups of users); 3) a Resource Indexing Functions database 18
which stores the definitions and logic for mapping specific
resources to specific context sets; and, 4) a historical User
Interaction Records database 15 which stores the users' prior
queries, responses, and interactions with the system 10. The first
three databases are created before system startup and the User
Interaction Records 15 is created with the first user/use of the
system, however, it is understood that all four databases are
maintained and enhanced through system operations described
below.
First, prior to a user signing on to the system, and before the
user first views the iconic interface 12, the system 10 performs
several pre-processing steps including: 1) creating of an empty
"user context vector" 25 and populating the context vector with
minimal information from external data elements 11 integrated with
the system or, from system sensing/discovery; and, 2) processing
the minimal user context vector 25 against the Context Attributes
database 14, the Attribute Value Functions database 16, and the
User Interaction Records database 15 using context classification
logic to result in a "suggestion" that this particular user may be
classified into one of a small number of user context definitions
from the system's predefined long list of context definitions.
After these pre-processing steps, the first iconic interface 12 is
then displayed for the user at the user's terminal, or web-browser,
in the case of resource searches conducted over a web-based
communication link. The iconic Context Selection Workspace 13
initially displays a small set of User Context Icons it has
determined are most appropriate, captures the user's selection of
the one that seems most fitting for the current user search
session, and captures the user's actual query. In most cases, this
minimal entry will suffice to begin the search because the system
has already determined the relevant attributes, default values and
parameters to drive the system forward through the user search
without any additional entry on the user's part. However, if the
user wishes to review their defaults or to fine tune some context
or resource variables, there is an option to proceed to the iconic
Detailed Specification Workspace display 22 before starting the
search.
Regardless of the screen navigation path chosen, when the user
initiates the query, the system 10 packages the user query with a
detailed User Context Vector 25 summarizing what is known of the
user's needs at this point. Once the search is initiated, the query
and context vector are processed sequentially through three
distinct sub-processes: 1) the Classifying User Contexts
sub-process 24 according to the invention; 2) an Adaptive Indexing
of Resource Solutions and Resource Lookup sub-process 28; and, 3) a
Response Set Ordering and Annotation sub-process 34 according to
the invention.
Particularly, the Classifying User Contexts sub-process 24,
receives as input the user query and the raw context vector 25 and
External User Data 11, and processes these against the User
Interaction records 19 for this user/user group, data from the
Context Attributes Master 14 and Attribute Value Functions 16. The
system classifies this specified user interaction state and
annotates the context vector 25' with a complete set of context
parameters for use in subsequent processing. The Classifying User
Contexts sub-process 24 particularly applies an inductive learning
algorithm as an attempt to predict derived contexts. Additionally,
the Classifying User Contexts sub-process 24 updates the Attribute
Value Functions database 16 with more enhanced functions. The
actual processing via Context Classifier and Context Applier is
described in greater detail in commonly-owned, co-pending U.S.
patent application Ser. No. 09,778,378 entitled CUSTOMER SELF
SERVICE SUBSYSTEM FOR CLASSIFYING USER CONTEXTS, the contents and
disclosure of which are incorporated by reference as if fully set
forth herein.
As the customer self service system is provided with functionality
enabling a user to "bookmark" their stopping point in a prior
session and to resume with a "work-in-process" data set, the
initial settings may be modified based upon system discovery or
user override at the time of inquiry, resulting in the raw contexts
associated with the user's current inquiry transaction. It is this
raw context data which serves as input to the context classifier
sub-process 24.
The Adaptive Indexing of Resource Solutions and Resource Lookup
sub-process 28 receives as input the user query and the context
vector 25' and processes them against a Resource Library 42, the
User Interaction Records for this user/user group 19, and the
Resource Indexing Functions 27. This sub-process particularly maps
specific contexts to specific resources so as to increase the
relevance of search results for a given user in their current
context without requiring the user to explicitly train the system.
The primary output of the Adaptive Indexing of Resource Solutions
and Resource Lookup sub-process 28 is a newly identified Resource
Response Set 35 which is input to the Response Set Ordering and
Annotation sub-process 34. The Adaptive Indexing of Resource
Solutions and Resource Lookup sub-process 28 additionally generates
a secondary output which comprises updates to the Resource Indexing
Functions database 18 with yet more enhanced functions 27'. Further
details regarding the Adaptive Indexing of Resource Solutions and
Resource Lookup sub-process 28 may be found in commonly-owned,
co-pending U.S. patent application Ser. No. 09,778,135 entitled
CUSTOMER SELF SERVICE SUBSYSTEM FOR ADAPTIVE INDEXING OF RESOURCE
SOLUTIONS, the contents and disclosure of which are incorporated by
reference as if fully set forth herein.
According to the invention, Response Set Ordering and Annotation
sub-process 34 receives as input the User Context Vector and
Resource Response Set 35 and processes it against data from an
Annotation Scoring Metric database 46 and User Interaction Records
19 for the particular user/group. This sub-process 34 weights and
ranks the potential responses according to the resource selection
criteria specified by the user on the Detailed Specification
Workspace described herein, and takes into consideration the
scoring metric. The sub-process 34 additionally tags the response
set with data elements necessary for display and manipulation on a
visualization system, including, but not limited to, the Results
Display Workspace 32 described in the co-pending U.S. patent
application Ser. No. 09/778,147, and particularly generates as
output an Annotated Resource Response Set 38.
More particularly, FIG. 6 is a flowchart depicting the response
ordering and annotation sub-process methodology 34 for ordering a
result set according to the preferred embodiment of the invention.
As shown in FIG. 6, the User Interaction Records 19 (which include
the actual resources selected by the users and the annotation
schemes used to present them) and the Annotation Scoring Metric 46
are input to an Adaptive Annotation Algorithm 341 which is a
supervised learning algorithm that generates functions for
optimally annotating the response set for ease of use as defined by
the Annotation Scoring Metric. For the purpose of this invention
the terms rule and function are used interchangeably. Both refer to
any data structure that can be executed by an interpreter in a way
as to compute a set of labeled output values given a set of labeled
input values. An example of an arithmetic rule is
"Fahrenheit<-Centigrade*5/9+32". Rule languages include but are
not limited to: neural nets, decision trees, functional languages,
polynomial functions. User Interaction Records 15 particularly
comprises traces of previous interactions with users of the system
including: all types of raw context information that were available
during those interactions, whether it be static, historical, or
transient, organizational or community context, environment
context, or any other context associated with the user and
dependent upon that user's interaction state and query domain,
e.g., education, real estate, travel, etc. user queries, the
system's responses, and, in addition, user feedback generated by
the user regarding the resources that were provided during those
sessions. User feedback, for example, may include a specification
of which resource was chosen by the user given a list of displayed
resources. The Annotation Scoring Metric 46, for example, may
include a parameter representing the measure of "goodness" in terms
of how easily the user may find the resources in the response set
35. As another example, the Annotation Scoring Metric 46 may be set
up to penalize an annotation which does not make it "easy" for the
user to find the resources in the response set, i.e., a metric that
places most of the resources ultimately selected by the user on a
second screen on the user interface or at the bottom of the first
screen. As another example, one measure of performance is closeness
of the selected items to the top of the response set (assuming that
the annotations of the response set specify an ordering of the
response set).
Each of the user interaction records and annotation metric serves
as a training set for learning an ordering and annotation function
343. That is, the adaptive annotation algorithm 341 is implemented
to optimize the annotation function 343 as measured by the feedback
in the received interaction records 19. That is, the annotation
function 343 accepts an annotated list of resources, along with the
user interaction records associated with the interactions that
happened when this annotated list was presented to the user and
returns a real value representing the performance of that
annotation. For example, an annotation evaluation metric of a score
computed by counting how far down from the top of the list was the
user's selection given the annotation. Thus, according to this
metric, a given annotation set would get the highest possible score
if it had placed the resource eventually selected by the user at
the top of the list of resources presented to the user. It should
be understood that this adaptive process 341 need not be
interactive, but may be performed in batch or off-line.
The sub-process methodology 34 further includes an ordering
annotation step 345, during which the ordering and annotation
function 343 which comprises the functions to be used in mapping
the user context vector 25' with the resource response set 35 in
order to generate an annotated response set 38. It is understood
that the ordering and Annotation step 345 is executed
interactively, e.g., at the time of every user query. It is the
application of the ordering and annotation function 343 to the user
context 25' and resource response set 35 that result in the
annotations 38 for the responses in the input response set, which
annotations control the presentation of the resources to the user.
As an example, these annotations may include ordering, which
resources to bold, which would be placed on the primary screen of
query results seen by the user and which would be placed on a
secondary screen requiring an additional step by the user such as
clicking on a button "give me additional resources", which
resources to gray out, etc.
As mentioned, the ordered and annotated set of resources that the
system has found to best match the user's initial query and related
subject and context variables may be displayed through any
visualization system, including, but not limited to, the intuitive
iconic interface 32 for visualizing and exploring the response set.
In that case, the annotations 38 specifically are used to inform
the iconic user interface 32 (FIG. 7) what resources to display in
response to the query and how to display them.
FIG. 7 illustrates in detail the third iconic graphical user
interface 32 described in greater detail in commonly-owned,
co-pending U.S. patent application Ser. No. 09/778,147. As shown in
FIG. 7, the graphical user interface 32 is divided into the
following sections: a section for displaying the Query Entry field
131 as entered on the prior interface screen (FIG. 4) and available
for editing; a section for displaying a navigation arrow 135 for
enabling the user to proceed back to the Detailed specification
Workspace 23 of FIG. 5, and arrow 136 for returning to the initial
Context Selection screen via the first iconic interface to initiate
a new query or select a different user context; and, a Results
Display Workspace 33 that enables the user to visualize and explore
the response set that the system has found to best match the user's
initial query and related subject and context variables and that
enables the user to continue working to learn about the resources
suggested (detail/preview), narrow their results (selection) or
re-display them in a more meaningful view for decision making
(graphically).
The Results Display Workspace 33 particularly includes a graphic
element 333 which comprises a list of ranked resources 338 returned
by the user's query. Via this graphic element, the user is provided
with ability to select via checkboxes 348, for example, one or more
resources for viewing of additional details. The response set 338
is ranked by the aggregate value and weighting defined by resource
selection criteria and value ranges as described herein.
As shown in FIG. 7, the Results Display Workspace 33 displays the
weighting 332 for each of the available resource selection criteria
339a, . . . , 339e. The choices of weighting and selection of
resource selection criteria are made on the Detail Specification
Workspace described generally herein with respect to FIG. 5.
Preferably, the system generates for display in the Results Display
Workspace 33 a multidimensional plot 335 comprising one or more
axes, e.g., 331a, . . . , 331e, with each axis corresponding to
each previously specified results selection criterion such as cost
339e, time 339a, timing 339b, quality 339d and risk 339c. The plot
is initiated in response to user selection of graph icon 337, and
the user's selection of one or more resources 338 from the
displayed list 333 of ranked resources. Each axis 331a, . . . ,
331e is displayed in the sequence specified by the user in the
detail specification workspace 23 and includes one or more data
points 349 corresponding to each resource 348 selected from the
list 333. Each data point represents the value of the particular
resource selection criteria represented by the axis for that
resource. As the user moves his/her mouse over a data point
resource on one of the axes 331a, . . . , 331e, for example, data
point 330a on axis 331a in FIG. 7, the resource represented by that
data point is visually connected, e.g., by line 334, to all the
other points for that same resource, e.g., points 330b-330e.
Additionally, in response to such showing, the values for all the
resource selection criteria and name and rank of the resource 342
is displayed. It is understood that the locations of the data
points 349 on each axis reside between the minimum and maximum
resource selection criteria values indicated by the slider bars
252a, 252b as previously set by the user in the detailed
specification workspace 23 of FIG. 5.
The interface 32 is additionally provisioned with an icon 346
selectable for initiating the display of a Resource Detail Display
portion 336 shown in FIG. 7, which is a graphical element used to
provide further details or previews of the resources 338 selected
from the list of ranked resources 333. Besides providing a text
description 329 of the resource, including name, cost, timing, and
terms and conditions, the graphical element 336 may be provided
with hyperlinks 351-353 enabling the user to read more details
regarding the resource, see pictures of the resource, or preview
the resource, respectively. It should be understood that icon 337
for viewing the graph or the icon 346 for viewing detailed
descriptions of the actual resources are independently
selectable.
As further shown in FIG. 7, the user has the additional option 347
to view a detailed description of a currently plotted resource
highlighted or shown in the graphic portion 335. The detailed
description of a currently plotted resource is displayed via the
Resource Detail Display portion 336.
As the user works with the system, particularly through the Results
Display Workspace 33 (FIG. 7) and the Detail Specification
Workspace 22 (FIG. 5) his/her interactions are captured and stored
in the User Interaction Records database 15. Thus, in addition to
the user query, context vector and response data set, the system
retains adjustments to user context, results display manipulation,
and results viewing and selection behavior 51.
Having completed the transaction, there is one more sub-process
which is essential to this system: the sub-process for Context
Cluster Discovery and Validation 48. This batch process, occurring
asynchronously and constantly, applies unsupervised (machine)
learning to cluster user interaction records and to assist in the
identification of new user contexts, attribute value functions and
resource indexing functions. The User Interaction Records 19 are
processed against the Context Attributes Master database 14, the
Attribute Value Functions database 16 and the Resource Indexing
Functions database 18 and a Distance Metric 44 which helps
determine "how close is close", i.e., "what's good enough" for a
variety of factors. When validated by a system administrator,
additional user contexts may be implemented (manually or
semi-automatically) in the databases and visibly as new icons on
the Context Selection Workspace 13.
Attribute functions may also be identified and resource indexing
functions may be discovered and updated in the appropriate files
automatically. All of these additional classifications improve the
ease of use, accuracy, and predictability of the system over time.
Further details regarding the Context Cluster Discovery and
Validation sub-process 48 may be found in commonly-owned,
co-pending U.S. patent application Ser. No. 09/778,149 entitled
CUSTOMER SELF SERVICE SUBSYSTEM FOR CONTEXT CLUSTER DISCOVERY AND
VALIDATION, the contents and disclosure of which are incorporated
by reference as if fully set forth herein.
The customer self-service system and the interaction with the
system through the iconic interfaces of FIGS. 2, 4, 5 and 7, will
now be described with respect to example domains such as education,
travel and real estate, and further will be described from the
point of view of the following users: a learner, a traveler and a
real estate transactor, e.g., renter/buyer. In describing the
user's interaction with the system through the iconic interfaces, a
set of data elements used in the system and their characteristics
are first defined as follows:
Query: an entry field for entering search data by using text or
voice methods, for example, but not limited to these methods
User Context: a User Context represents a predefined set of context
attributes which are relevant to the search behavior/needs of a
group of people.
More particularly, the User Context enables the packaging of a rich
set of attributes about the user with a rich set of attributes
about their searching and execution environment in response to "one
click" of an icon for the user presented via the interface. While
there are potentially a large number of potential user contexts for
any user population, each individual user would likely settle on a
small number that apply to them in different circumstances. The
naming of these contexts is important so that the user may
recognize him/herself as potentially fitting into that group. The
attributes associated with a particular user context are predefined
by system administration and cannot be modified by the user. Over
time, by implementing the Classifying User Context sub-process 24
(FIG. 1), the system will identify changes to the attribute set
that will make a particular user context perform better for its
repeated users. Over time the system will detect different
attribute sets which appear to predict user needs/behaviors and
might suggest new user contexts for the system.
Context Attribute: An attribute is used to describe a
characteristic associated with the User Context.
There are potentially an unlimited number of attributes defined to
the system with a master list maintained in the Context Attributes
Master File. New attributes are discovered and added with system
administrator validation. End users may not modify the definition
of a context attribute, nor its' packaging into user contexts, nor
the list of values associated with each.
Attribute Value: A list of attribute value choices is predefined
for each context attribute.
The system sets a default value to each attribute based upon data
lookup, sensed, or historically derived from prior user entry or
behavior. Either the system or the user may modify the value
initially set based upon explicit preferences or observed behavior.
This value is added to the context vector used for resource lookup,
and is retained in the historical User Interaction Records database
15 so it may be used to set default values for each individual each
time they use the system.
Value Resource Parameters: Parameters defined in terms of inclusion
and exclusion that may be used as a filter to increase the
relevance of the response set.
That is, with the basic search logic established, the user's query
may be satisfied. However, the response set may contain a large
number of resources which are not satisfactory to this individual.
Value Resource Parameters defined in terms of inclusion and
exclusion may be used as a filter to increase the relevance of the
response set. The inclusionary parameters may be easier to
establish by users new to the system and that exclusionary
parameters will become more evident as users gain experience in
working with the response sets.
Resource Selection Criteria and Value Ranges: Parameters and
specifications for ranking a user's response set to enable more
informed resource selection.
Thus, even with the degree of specificity enabled by the system,
and even with the constant improvement in search
relevance/efficiency as it relates to user contexts, there usually
may be more than one resource to present to the user (in fact, if
the search is too narrow, the user may miss the opportunity to
explore/discover different approaches to meeting their actual
needs). As most users know (or think they know) the criteria they
will apply to selecting between options, a limited set of resource
selection criteria are provided by the system (the set would differ
by domain). However, via an interactive graphical display provided
by the iconic interface of the invention, the user may now specify
acceptable value ranges and relative weighting of each criteria for
ranking their response set and/or may customize the use of these
criteria.
When the actual response set data is offered, most users face the
reality of many options, few options, more subjective information
about specific resources; and they may make tradeoffs around the
selection logic. For example, the response set may be refreshed as
the user may decide to eliminate a criteria, change the weight of a
criteria, or change the acceptable value ranges for a criteria.
From these specifications, accessible via the iconic interface of
the invention, the user may determine for example, whether time,
timing, flexibility, and risk may be sacrificed in order to bring
the cost down below a certain dollar ($) value, and, for example,
determine how much more would the user need to pay to get exactly
what he/she wants exactly when he/she wants it.
FIGS. 2, 4, 5 and 7 depict in greater detail the iconic interfaces
for the customer self service system that enable the use of a rich
set of assumed, sensed, inferred, and derived contexts with minimal
user effort.
With initial logon, as shown in FIG. 2, the system first presents a
set of user contexts which are available to the user via the
simplified iconic interface 12 of FIG. 2. The system will suggest
one context over the others, but the user may select the one most
appropriate to their current situation. In each session, the user
selects only one user context to use, however over time each user
may discover that a couple of different user contexts serve their
needs in differing circumstances. On this screen 13 particularly,
the user then enters a query via one or more methods including text
via a web browser display interface, for example, or via voice, for
example, with help of voice recognition software. It should be
understood however, that query entry is not limited to these types
of methods. The user will then initiate a lookup and proceed either
to a third process step (via most direct path 52) for viewing a
search result response set via the Results Display Workspace
interface 32, or, proceed to a second step (via path 50) to
optionally refine/override search variables via the Detail
Specification Workspace interface 22.
FIG. 4 illustrates in detail the first graphical user interface 12
including the initial Context Selection Workspace 13 that enables
the expression of user context as part of a query. As shown in FIG.
4, the Context Selection Workspace 13 includes: a series of one or
more selectable User Context Icons 132 presented to the user for
selecting user contexts; and, a Query Entry Field 131 enabling user
entry of search terms via text or voice entry, for example. In
accordance with the principles of the invention, the User Context
Icons 132 are graphical user interface elements from which the user
selects the one context most representative of his/her current
situation. The icons presented in this interface each represent a
packaging of sets of attribute-value pairs which describe a kind of
user in a particular situation. Particularly, a user context
represents a predefined set of context attributes which are
relevant to the search behavior/needs of a group of users. For
example, as described herein, context may include aspects of the
user's knowledge, their relationship to organizations and/or
communities, their user environment(s), and their resource need.
All of these combine to provide a rich context surrounding the
actual query which can significantly improve the outcome of the
search through resources.
The Context Selection Workspace 13 thus enables the expression of
user context as part of the query and is optimized for ease of use.
Particularly, the user selects from one or more of the several
displayed context icons 132 by clicking on them. A context
"applier" pre-process described in commonly-owned, co-pending U.S.
patent application Ser. No. 09/778,378 is invoked at each session
initiation for a user's search transaction, using a minimal or null
user data set to produce defaults for user context, attributes,
values, and resource parameters for the initial display of the
Context Selection Workspace 13. This pre-processing step delivers
additional benefits to the user by ensuring the use of the most
current data and functions operating in the system. After making
the initial query entry, by selecting hyperlink 134, the user is
able to initiate the search and proceed directly to the third
interface 32 which displays the actual search results. Alternately,
by selecting hyperlink 135, the user may proceed to the second
interface 22 having the Detail Specification Workspace 23 for
further query editing and/or context refinement.
Returning to FIG. 2, with respect to the second step, the user is
able to fine tune or override context attribute values, value
resource parameters, and resource selection criteria and value
ranges, using a drag and drop interface, iconic pulldowns, and/or
slide buttons. The user may return to this screen as many times as
needed to find a suitable response set. Particularly, via the
second iconic interface 22, the User Context selected in the first
step has been made explicit by its default settings on all the
iconic interface elements listed. Thus, via a Detail Specification
Workspace 23 the user may: 1) modify the query (via text entry or
voice, for example); 2) change the value of attributes associated
with the user context (using pull down menus); alter the value
resource parameters (e.g., include/exclude) using checkboxes; 3)
customize the subset of responses by altering the resource
selection criteria, including the weighting of criteria and the
ordering of criteria on the final display, (e.g., using checkbox
and/or numeric entry); and, 4) further refine the selection by
specifying minimum/maximum acceptable value ranges for resource
selection criteria through drag and drop of "tabs" on sliders, for
example. After making the necessary adjustment, the user
re-initiates the lookup and may proceed to the third step via path
51.
FIG. 5 illustrates in detail aspects of the second iconic graphical
user interface 22 which enables the user to define or change all
the parameters associated with their query 131 and (single)
selected user context 132. As shown in FIG. 5, the graphical user
interface 22 is divided into the following sections: a section for
displaying the Query Entry field 131 as entered on the prior
interface screen (FIG. 4) and available for editing; a section for
displaying navigation arrows which allow the user to proceed with
the search 134, or return to the initial Context Selection screen
136 via the first iconic interface to initiate a new query or
select a different user context; and, a Detailed Specification
Workspace 23 which is where all the search parameters can be
explicitly viewed and modified. There are only two things the user
cannot change from this screen: the user context selected (which
they may change only on the Context Selection screen) and the
context attributes which are linked to the user context (and which
are predefined in the Context Attributes Master database 14).
As shown in FIG. 5, within the Detailed Specification Workspace 23
there comprises: an Attribute-Value Workspace 231, for enabling the
user to change the attribute values for all the context attributes,
represented as graphic elements 232, associated with the selected
user context icon 132 (FIG. 4); and, a Resource Selection Criteria
Workspace 238, for enabling the user to define the criteria 245 to
be used in evaluating resources, define minimum and maximum
acceptable values provided on slider elements 250 corresponding to
each criteria, specify the weight assigned to those criteria via
selection boxes 242, and specify the positioning of those criteria
in a graphical display of the resources selected via selection
boxes 241. As will be described, FIG. 3 provides sample data for
the context attribute, attribute value, value resource parameters,
and partial resource selection criteria from different domains
which may be represented in the Detailed Specification Workspace
23.
With more particularity, the Detailed Specification Workspace 23
additionally includes the Value-Resource Parameter Workspace 235,
for enabling the user to change or create resource parameters using
include logic 237 or exclude logic 239 for any attribute value 232
selected in the Attribute-Value Workspace 231. More specifically,
the Attribute-Value Workspace 231 includes graphical
representations of all the context attributes 232 associated with
the single (currently active) selected user context 132. Each
context attribute 232 is displayed with a text title 233 for the
attribute. The currently active attribute value for that context
attribute is shown on each context attribute icon. In addition, if
the user has substituted, as described below, a context attribute
value different than the default value provided for this user
session, a marker 253 is displayed on the corner of the context
attribute icon. If the user "mouse clicks" on the context attribute
element, e.g., icon 232b, the system displays a pull down menu 234
of graphic elements showing all the possible attribute values for
this context attribute. If the user "mouses over" any of the values
from pull down menu 234, e.g., attribute value 236, a textual
description 236' supporting the element may appear. By selecting a
context attribute element from the pull down menu 234, e.g.,
element 236 shown highlighted in FIG. 5, the user is enabled to
fine tune their selected context based upon their current
situation. If the user "mouse clicks" on a value other than the
current default, the new value is "selected" to substitute for the
default. If the user "double clicks" on the attribute value, the
system prepares the Value-Resource Parameter Workspace 235 for this
single attribute value, as will be described. FIG. 3 provides
sample data for context attributes and attribute values from
different domains which may be represented in the Attribute Value
Workspace 231.
In the Value-Resource Parameter Workspace 235, the user may change
or create resource parameters using include logic or exclude logic
for any context attribute value 232 selected in the workspace 231.
Regarding FIG. 5, with more particularity, the Value-Resource
Parameter Workspace 235 is displayed for one attribute value at a
time and is only displayed when requested via a double click, for
example, on one of the attribute values displayed in the attribute
Value Workspace 231, e.g., attribute value 236. The Value-Resource
Parameter Workspace 235 is a pre-formatted two-column space (dialog
box) where the user may establish inclusionary resource filters via
checkboxes 237 and/or exclusionary resource filters via checkboxes
239, based upon pre-established resource characteristics 236" for
that selected attribute value. The value resource parameter data
elements are pre-set by the user's know context, prior history of
selecting from resources identified by the system, and potentially
by corporate/organizational policy implemented through the system.
By making these additional specifications, the user is enabled to
increase the relevance of the resource response set based upon
their current situation and personal preferences. When finished
with these specifications, the user may double click to close this
box 235 and return to the Attribute Value Workspace 231. This step
can be repeated for as many attribute values as the user would like
to refine and may be executed either before or after the search is
conducted. Value resource parameter data elements associated with
context attribute values for different domains, are provided in
FIG. 3 as samples of data which may be represented in this
Value-Resource Parameter Workspace 235.
Regarding FIG. 5, with more particularity, the Resource Selection
Criteria Workspace 238 includes a list of criteria 245 which may be
used in evaluating resources. This list, provided by the system, is
customized by domain; but in all domains, it involves criteria
including, but not limited to issues such as: cost, time, timing,
quality and risk associated with using a particular resource to
satisfy the user's specific need. The initial system default might
be to use all criteria and weight them equally. Over time, however,
the default criteria may be set by the system based upon user
context, user prior transaction history and user behavior on prior
searches. If the user wishes to further reduce the set of criteria,
they may do so by assigning a weight, for example a percentage
weight, to each criteria they want used in the entry boxes 242.
Along with each of the criteria selected there exists a range of
acceptable values specified on an associated individual slider
element 250. The initial system default, may be "unlimited" and
then, may be set over time based upon user context, use and
behavior. Additionally, the user may use drag and drop tabs 252a,b
on the slider element 250 to set a minimum and/or maximum value for
the associated resource selection criteria. It is understood that
the unit of measure on the sliders may vary by criteria. Further,
via entry boxes 241, the user may select to view via "check" or
specify via number entry the display sequence of these criteria
when arrayed as the axes on an n-dimensional graphic display
provided in the Results Display Workspace via graphic interface 32
as described in commonly owned, co-pending U.S. patent application
Ser. No. 09/778,147, or when viewed on another visualization
system.
The Detailed Specification Workspace 23 thus provides full
disclosure of system defaults and enables the user to completely
manage their search.
With respect to the third step, a display of the annotated response
set is provided in a form ready for preview or selection as
described herein with respect to FIG. 7. The user may rework this
screen as many times as needed to better understand and make
decisions about resource(s) to use. More particularly, via the
Results Display Workspace 33 the user may: 1) view the response
set, ranked by the aggregate value and weighting as defined by
resource selection criteria and value ranges; 2) select one or many
of the ranked responses for graphical display in multi-dimensions
along the multiple axes of the resource selection criteria; and, 3)
initiate a "roll over" of one or more resources from either the
ranked list or the graphical display to view detailed descriptions
or to "preview" the resource. If there are too many responses, too
few, or if they are incorrect, the user may return to the second
step to further refine/redefine, and re-execute the lookup.
Alternately, the user may return to the first step to choose a
different context for their search.
While the system is intended to operate on a fully enabled graphic
workstation or personal computer, it is intended that search
definition and the results visualization processes described herein
with respect to FIGS. 4, 5 and 7 may be operated by users of
reduced graphics-enabled devices such as text screen workstations,
Organizers, or any type of Personal Digital Assistants (PDAs).
Accordingly, in alternative embodiments, all the context icons may
have names, all the graphical displays may be reduced to lists, all
the pull downs may be viewed as indented lists or secondary
screens, and all the min-max sliders may convert to fill-in boxes.
Further, as mentioned, the customer self service system described
herein is applicable to many applications including the domains of
education, real estate, and travel. The generic process flow
described with respect to FIG. 2, will now be described with
specific examples from the education, real estate and travel
domains as shown in FIG. 3.
With respect to the education domain, the user is a learner and
FIG. 3 depicts an example interaction with the system through the
iconic interfaces (FIG. 2) included in the embodiment of the
invention as applied to the education domain. The three iconic
workspaces of FIG. 2 enable the learner to specify example data
elements, such as the example data elements depicted in the
Education (e.g., Environmental) column 60 of FIG. 3, and view
results, as follows: In the first process step, the learner uses
the Context Selection Workspace (interface 12 of FIG. 4) to specify
their query 61 as "Learn Lotus Notes at home." The learner may
select the User Context "Remote Staffle", for example (where the
icon's name is highlighted in FIG. 3), from among the available set
of context icons 62. The learner may then elect to go to the Detail
Specification Workspace (interface 22 of FIG. 5) in the second
process step in order to view the context attributes 63 associated
with the "Remote Staffie" User Context. Preferably, the default
assigned context attribute value ("DSL", for example) for any
context attribute ("Connectivity", for example) is visible on the
context attribute icon ("Connectivity", for example, whose name is
shown highlighted in FIG. 3). The learner may click on the context
attribute "Connectivity" to see the menu of associated attribute
values 64. The learner, for example, may select the "Disconnected"
attribute value shown highlighted in FIG. 3. By double clicking on
this attribute value the list of Value Resource Parameters, i.e.,
include/exclude filters 65, for the attribute value "Disconnected"
is displayed. The learner, for example, may indicate that they want
to include download and play resources and exclude online
collaborative resources when searching for relevant resources. The
learner may additionally specify resource priorities 66 by
selecting, sequencing and weighting and specifying minimum and
maximum values for relevant criteria such as cost, time, quality
and risk on the Resource Selection Criteria Definition graphical
user interface element on the Detail Specification Workspace
(interface 22 of FIG. 5). In the third step of the process, the
results of the learner's search are listed in the user view of the
Results Display Workspace (interface 32 of FIG. 2). The learner may
immediately select one or more of the listed education resources,
request to see additional details on them, or request to see a
response set graphic indicating the relative positioning of each
resource along each of the axes (n-dimensions, relating to cost,
time, quality and risk) specified earlier. If no acceptable
education resources were provided, the learner may return to the
Context Selection Workspace to redefine their query or select a
different User Context such as "Commuting Techie" via the first
interface. The learner may additionally elect to return to the
Detail Specification Workspace of the second interface to change
the default value of the context attribute "Connectivity" from
Disconnected to Dial-up and add or remove Value Resource Parameters
for the attribute value Dial-up or other context attribute values
associated with context attributes such as "Learning Mode" or
"Technical Field". The learner may also change their selection
criteria, the weighting of the selection criteria, and the
minimum/maximum values for any selection criteria, in hopes of
identifying additional relevant resources.
With respect to the education domain, the user is a "learner"
however, the three iconic workspaces of FIG. 2 provide the process
for enabling the learner to specify example data elements, such as
the example data elements depicted in the Education (e.g., Subject
Matter) column 70 of FIG. 3, and view results, as follows: In the
first process step, the learner uses the Context Selection
Workspace (interface 12 of FIG. 4) to specify their query 71 as
"Become a Linux developer by June" for example. The learner selects
the User Context "Commuting Techie" from among the available
context icons 72. The learner may elect to go to the Detail
Specification Workspace in order to view the context attributes 73
associated with the "Commuting Techie" user context. Preferably,
the default assigned context attribute value ("Programming", for
example) for any context attribute ("Technical Field", for example)
is visible on the context attribute icon ("Technical Field", for
example, whose name is shown highlighted in FIG. 3). In addition,
the learner may click on the context attribute ("Technical Field,
to stay with the example) to display a pull down menu to view the
other values 74 (in either picture or word format) that could be
assigned to this attribute. The learner, for example, may select
"Graphical Interfaces" shown highlighted in FIG. 3. By double
clicking on this attribute value, the list of Value Resource
Parameters (include/exclude filters 75) for the attribute value
"Graphical Interfaces" will be displayed. For example, the learner
may indicate that they want to include the KDE interface and
exclude the GNOME interface when searching for relevant resources.
The learner may additionally specify resource priorities 76 by
selecting, sequencing and weighting and specifying minimum and
maximum values for relevant criteria such as cost, time, quality
and risk on the Resource Selection Criteria Definition graphical
user interface element on the Detail Specification Workspace. The
results of the learner's search are listed on the Results Display
Workspace via the interface 32. The learner may immediately select
one or more of the listed education resources, request to see
additional details on them, or request to see a response set
graphic indicating the relative positioning of each resource along
each of the axes (n-dimensions, relating to cost, time, quality and
risk) specified earlier. If no acceptable education resources were
provided, the learner may return to the Context Selection Workspace
13 via the first interface 12 to redefine their query or select a
different user context such as "Traveling Consultant." The learner
may also elect to return to the Detail Specification Workspace via
the second interface 22 to change the default value of the context
attribute "Technical Field" from Graphical Interfaces to
Programming and add or remove Value Resource Parameters for the
attribute value Programming or other context attribute values
associated with context attributes such as "Learning Mode" or
"Connectivity." The learner may also change their selection
criteria, the weighting of the selection criteria, and the
minimum/maximum values for any selection criteria, in hopes of
identifying additional relevant resources.
With respect to the real-estate domain, the user is a real estate
transactor (renter/buyer) and FIG. 3 depicts an example interaction
with the system through the iconic interfaces (FIG. 2) included in
the embodiment of the invention as applied to the real estate
domain. The three iconic workspaces of FIG. 2 enable a real estate
renter or buyer to specify example data elements, such as the
example data elements depicted in the Real Estate column 80 of FIG.
3, and view results, as follows: In the first process step, the
renter or buyer uses the Context Selection Workspace to specify
their query 81 as "Find housing near new job by August." The renter
or buyer selects the user context "Relocating Business
Professional" from among the available context icons 82. The renter
or buyer may elect to go to the Detail Specification Workspace in
the second interface in order to view the context attributes 83
associated with the "Relocating Business Professional" user
context. Preferably, the default assigned context attribute value
("Subcontract it all", for example) for any context attribute
("Maintenance Style", for example) is visible on the context
attribute icon ("Maintenance Style", for example, whose name is
shown highlighted in FIG. 3). In addition, the renter/buyer may
click on the context attribute ("maintenance style, to stay with
the example) to display a pull down menu to view the other values
84 (in either picture or word format) that could be assigned to
this attribute. Upon renter or buyer double clicking on attribute
value "Do-It-YourSelf-er", for example, the list of Value Resource
Parameters (include/exclude filters 85) for the attribute value
"Do-It-YourSelf-er" is displayed. For example, as shown in FIG. 3,
the renter or buyer may indicate that they want to include walls,
paint and lawn mowing and exclude plumbing, electrical and
landscaping when searching for relevant resources. The renter or
buyer may additionally specify resource priorities 86 by selecting,
sequencing and weighting and specifying minimum and maximum values
for relevant criteria such as cost, time, quality and risk on the
Resource Selection Criteria Definition graphical user interface
element on the Detail Specification Workspace. The results of the
renter or buyer's search are listed on the Results Display
Workspace of the third interface 32 in which the renter or buyer
may immediately select one or more of the listed real estate
resources, request to see additional details on them, or request to
see a response set graphic indicating the relative positioning of
each resource along each of the axes (n-dimensions, relating to
cost, time, quality and risk) specified earlier. If no acceptable
housing resources were provided, the renter or buyer may return to
the Context Selection Workspace to redefine their query or select a
different user context such as "Empty Nester." The renter or buyer
can also elect to return to the Detail Specification Workspace to
change the default value of the context attribute "Maintenance
Style" from Do-It-Yourself-er to Subcontract It All, for example,
and add or remove Value Resource Parameters for the attribute value
"Subcontract It All" or other context attribute values associated
with context attributes such as "Mode of Commute to Work/School" or
"Mode of Housing." The real estate transactor may also change their
selection criteria, the weighting of the selection criteria, and
the minimum/maximum values for any selection criteria, in hopes of
identifying additional relevant resources.
With respect to the travel domain, the user is a traveler and FIG.
3 depicts an example interaction with the customer self service
system through the iconic interfaces (FIG. 2) included in the
embodiment of the invention as applied to the travel domain. The
three iconic workspaces of FIG. 2 enable a traveler to specify data
elements, such as the example data elements depicted in the Travel
column 90 of FIG. 3, and view results, as follows: In the first
process step, the traveler uses the Context Selection Workspace to
specify their query 91 such as "Plan a trip to Vermont in June",
for example. The traveler may then select the User Context Icon
"Single Mom with kids", for example, from among the available user
context icons 132, (where the icon's name 92 is highlighted in FIG.
3). The traveler may then elect to go to the Detail Specification
Workspace in order to view the context attributes 93 associated
with the "Single Mom with Kids" user context.
Preferably, the default assigned context attribute value ("Drive",
for example) for any context attribute ("Mode of Transportation",
for example) is visible on the context attribute icon ("Mode of
Transportation", for example, whose name is shown highlighted in
FIG. 3). In addition, the traveler may click on the context
attribute ("mode of transportation ", to stay with the example) to
display a pull down menu to view the other values 94 (in either
picture or word format) that could be assigned to this attribute
("Fly" for example). The traveler selects "fly" as an alternative
to "drive", as illustrated with highlighting in FIG. 3. By
"overriding " this attribute value and double clicking on it, the
list of Value Resource parameters (include/exclude filters 95) for
the attribute value "Fly" is displayed. The traveler may indicate
that he/she wants to include all major carriers and exclude prop
planes and airlines with bad safety records when searching for
relevant resources. The traveler may also specify resource
priorities 96 by selecting, sequencing and weighting and specifying
minimum and maximum values for relevant criteria such as cost,
time, quality and risk on the Resource Selection Criteria
Definition graphical user interface element on the Detail
Specification Workspace. The results of the traveler's search are
then displayed via the Results Display Workspace of the third
iconic interface 32 of FIG. 2. The traveler may immediately select
one or more of the listed travel resources, request to see
additional details on them, or request to see a response set
graphic indicating the relative positioning of each resource along
each of the axes (n-dimensions, relating to cost, time, quality and
risk) specified earlier. If no acceptable travel resources were
provided, the traveler may return to the Context Selection
Workspace in Step 1 to redefine their query or select a different
user context such as "Swinging Singles." The traveler may also
elect to return to the Detail Specification Workspace in Step 2 to
change the default value of the context attribute "Mode of
Transportation" from Fly to Train and add or remove Value Resource
Parameters for the attribute value Train or other context attribute
values associated with context attributes such as "Mode of Housing"
or "Food Style". The traveler may also change their selection
criteria, the weighting of the selection criteria, and the
minimum/maximum values for any selection criteria, in hopes of
identifying additional relevant resources.
Referring back to FIG. 1, the customer self service system
implements an n-dimensional context vector 25', derived from the
combination of user context and previous interaction with the
system, to map specific contexts to specific resources. This
increases the relevance of search results for a given user in their
current context without requiring the user to explicitly train the
system. Inferences and conclusions are made regarding both the
individual user's preferred resource characteristics and those of a
common set of users. These are used as input to the sub-processes
of the invention described herein and in sub-systems described in
above-mentioned commonly-owned, co-pending U.S. patent application
Ser. Nos. 09/778,378, 09,778,135 and, to modify the ionic
interfaces presented to each particular user for their subsequent
search using the current invention as well as to modify the results
that would be selected for presentation to the user via the
interface described in Ser. No. 09,778,147 in response to an
identical search. Over time, the system will improve in its ability
to serve individual needs and evolve to an ability to suggest
preferred answers to groups of users.
The overall system also uses a batch background process described
in commonly-owned, co-pending U.S. patent application Ser. No.
09,778,149 to cluster user interaction records to assist in the
identification of new user contexts which serves to improve the
system over time.
While the prior art has made use of adaptive learning in
information retrieval systems, the overall customer self service
system for resource search and selection enables the use of a
large, rich set of contextual attribute-value pairs, is focused on
learning about the user/user groups rather than the
resources/resource groups and is able to discover user group
characteristics and apply them to individuals. Much of the prior
art is focused on the discovery of database structure, the
clustering of data within the resources, or discovering relevant
taxonomy for resources but the current system discovers contexts
and context attributes among users which can be used predictively.
The customer self-service system of the invention uses a highly
specialized and optimized combination of supervised &
unsupervised logic along with both automated and semi-automated
entry of learned results and is able to deliver higher value
because contexts are used in a closed loop self improvement system;
front end (entry) middle (search and display) and back end (results
and user feedback) are integrated. Other systems apply machine
learning at the front, middle, or back, but not integrated
throughout. The current system identifies context classifications
and functions, and applies them to individual users to reduce the
burden of fully communicating their question and increasing the
specificity and accuracy of a query's search parameters. The
current system identifies and improves selection logic and
identifies and improves response sets to common queries based upon
a rich set of contextual variables. The current system additionally
orders the response set, potentially further limiting it, and
prepares the response set for display in a way that identifies the
"best" resources for a particular user based upon the rich set of
context variables. The display of the invention additionally
illustrates the decision making characteristics of the alternatives
presented.
While the invention has been particularly shown and described with
respect to illustrative and preformed embodiments thereof, it will
be understood by those skilled in the art that the foregoing and
other changes in form and details may be made therein without
departing from the spirit and scope of the invention which should
be limited only by the scope of the appended claims.
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